Research Article
Forecasting Financial Distress of Listed Firms Based on Recurrent Attention Networks
@INPROCEEDINGS{10.4108/eai.29-3-2024.2347392, author={Ming Jia}, title={Forecasting Financial Distress of Listed Firms Based on Recurrent Attention Networks }, proceedings={Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29--31, 2024, Wuhan, China}, publisher={EAI}, proceedings_a={ICBBEM}, year={2024}, month={6}, keywords={self-attention; financial distress prediction; bert; text processing}, doi={10.4108/eai.29-3-2024.2347392} }- Ming Jia
Year: 2024
Forecasting Financial Distress of Listed Firms Based on Recurrent Attention Networks
ICBBEM
EAI
DOI: 10.4108/eai.29-3-2024.2347392
Abstract
Financial distress not only poses a threat to the long-term survival of a company, but also may have a chain reaction on the whole economic system. In recent years, the use of textual information as a feature of financial distress prediction has become a new hotspot, and this study proposes a financial text processing model based on recurrent attention network (RAN). Through an empirical study of A-share listed companies from 2007 to 2019, it is found that the RAN model performs well in extracting information from annual reports and effectively improves the accuracy of financial distress prediction.
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